Cluster-Linkage Analysis in Traffic Data Clustering for Development of Advanced Driver Assistance Systems

Abstract

In terms of vehicle safety, the number of advanced driver assistance systems (ADAS) mounted in an automobile has been increasing recently. For an efficient conceptional design and system validation of ADAS, the representative test scenarios are indispensable. In order to identify the representative scenarios,the real-world traffic scenarios are to be clustered according to their similarity. The hierarchical agglomerative clustering is a well-known method to quantify the similarity of traffic scenarios existing in a database. However, the cluster structure is affected by the linkage criterion used in the agglomerative procedure.This study inquires into the similarity measurement of vehicle-pedestrian near-crashes in the USA. Various linkage criteria are selected to get better understanding of their influence on the clustering results and conduct a comparative study. Furthermore,a hybrid clustering algorithm is presented, which is based on k-covers and k-means clustering. Using the average silhouette width, the optimal number of clusters is calculated and the cluster structures are investigated. In the end, the representative scenarios are selected with the use of centrality measure and form the basis of the scenario catalog making for the reduction of test effort in ADAS development.